From Design to Implementation to Practice a Learning by Teaching System: Betty’s Brain

Biswas, Gautam; Segedy, James R.; Bunchongchit, Kritya

doi:10.1007/s40593-015-0057-9

Cited by 112 publications

(46 citation statements)

References 29 publications

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“…A teachable learning companion-Betty's Brain-has been shown to help learners consolidate their knowledge. 43 Based on education theories such as learning-by-teaching 44 and argumentative learning, 45 VLCs can help learners organize what they have learnt into coherent knowledge by facilitating discussion and seeding reflections. Interactions with VLCs can also generate learning behaviour data to facilitate learning skill analytics.…”

Section: Personalizing Learning Supportmentioning

confidence: 99%

Towards AI-powered personalization in MOOC learning

Chen

Leung

et al. 2017

npj Science Learn

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Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students' learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed.

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Section: Personalizing Learning Supportmentioning

confidence: 99%

Towards AI-powered personalization in MOOC learning

Chen

Leung

et al. 2017

npj Science Learn

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“…However, it was also found that some students lacked some of the pre‐requisite skills and their wider rationale, eg, about reasoning along causal chains, so as to learn about the specific causal relationships in the eco‐system to be modelled (Biswas, Segedy, & Bunchongchit, ). In order to improve the system's effectiveness, the introductory narrative was augmented to explain the nature of this reasoning and to make it clearer that this kind of reasoning would have many other applications.…”

Section: Screen‐level Pedagogy: Example Systemsmentioning

confidence: 99%

“…In the transfer test, the benefits of the guided learning became even more visible as the SRL students were able to transfer their metacognitive strategies to a new learning environment, where most of the scaffolds to aid the learning task were removed. (Leelawong & Biswas, 2008, p. 205) However, it was also found that some students lacked some of the pre-requisite skills and their wider rationale, eg, about reasoning along causal chains, so as to learn about the specific causal relationships in the eco-system to be modelled (Biswas, Segedy, & Bunchongchit, 2016). In order to improve the system's effectiveness, the introductory narrative was augmented to explain the nature of this reasoning and to make it clearer that this kind of reasoning would have many other applications.…”

Section: Learning By Teachingmentioning

confidence: 99%

Escape from the Skinner Box: The case for contemporary intelligent learning environments

Boulay¹

2019

Brit J Educational Tech

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Intelligent Tutoring systems (ITSs) and Intelligent Learning Environments (ILEs) have been developed and evaluated over the last 40 years. Recent meta‐analyses show that they perform well enough to act as effective classroom assistants under the guidance of a human teacher. Despite this success, they have been criticised as embodying a retrograde behaviourist technology. They have also been caught up in broader controversies about the role of Artificial Intelligence in society and about the entry of big data companies into the education market and the harvesting of learner data. This paper concentrates on rebutting the criticisms of the pedagogy of ITSs and ILEs. It offers examples of how a much wider range of pedagogies are available than their critics claim. These wider pedagogies operate at both the screen level of individual systems, as well as at the classroom level within which the systems are orchestrated by the teacher. It argues that there are many ways that such systems can be integrated by the teacher into the overall experience of a class. Taken together, the screen‐level and orchestration‐level dramatically enlarge the range of pedagogies beyond what was possible with the “Skinner Box.”

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“…For the researcher/designer, the behaviour of all of the other actors is available as data. The design process can take as a starting point the position of the teacher as the user of learning designs (Biswam, Segedy, & Bunchongchit, 2016) or it may include the behaviour of the teacher, for instance in feedback used to redesign courses (Morse, 2014). Almost as a self-evident matter, the researcher's behaviour is off-limits.…”

Section: Actionable Insight Translates Into Changing Student Behaviourmentioning

confidence: 99%

What Constitutes an ‘Actionable Insight’ in Learning Analytics?

Jørnø

Gynther

2018

Learning Analytics

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The possibilities of Learning Analytics as a tool for empowering teachers and educators have created a steep interest in how to provide so-called actionable insights. However, the literature offers little in the way of defining or discussing what the term "actionable insight" means. This selective literature review provides a look into the use of the term in current literature. The review points to a dominant perspective in the literature that assumes the perspective of a rational actor, where actionable insights are treated as insights mined from data and subsequently acted upon. It also finds evidence of other perspectives and discusses the need for clarification of the term in order to establish a more precise and fruitful use of the term. Notes for Practice• At the time of this review, we only found a single source in the literature that discusses the definition of "actionable insights." All other sources task the reader to infer the meaning of the concept from its use.• This paper provides a selective overview of learning analytics literature that sheds light on the use of the concept and its equivalents.• We identify a widely used perspective taken in learning analytics, which we dub "data-informed decision-making." This perspective is characterized by an insistence of the perspective of a rational actor and the use of learning analytics for the institutional goal of increasing retention.• We contend that "actionable insights" should be interpreted as data that allows a corrective procedure, or feedback loop, to be established for a set of actions.• We argue that the field of learning analytics would benefit from greater attention to the role of perspective and action capabilities in determining what "actionable insights" are.• The implications of these findings are that the perspective of data-informed decision-making is challenged, and with it, the idea that the presence of data alone provides the basis for determining insights. Instead, it charges any learning analytics researcher to map out the workflow of actions, the end goals of the actors involved, and the relevant couplings between them.

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From Design to Implementation to Practice a Learning by Teaching System: Betty’s Brain

Cited by 112 publications

References 29 publications

Towards AI-powered personalization in MOOC learning

Towards AI-powered personalization in MOOC learning

Escape from the Skinner Box: The case for contemporary intelligent learning environments

What Constitutes an ‘Actionable Insight’ in Learning Analytics?

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